A pair of readers of bivalent chromatin mediate formation of Polycomb-based "memory of cold" in plants.

The Polycomb-group chromatin modifiers play important roles to repress or switch off gene expression in plants and animals. How the active chromatin state is switched to a Polycomb-repressed state is unclear. In Arabidopsis, prolonged cold induces the switching of the highly active chromatin state at the potent floral repressor FLC to a Polycomb-repressed state, which is epigenetically maintained when temperature rises to confer "cold memory," enabling plants to flower in spring. We report that the cis-acting cold memory element (CME) region at FLC bears bivalent marks of active histone H3K4me3 and repressive H3K27me3 that are read and interpreted by an assembly of bivalent chromatin readers to drive cold-induced switching of the FLC chromatin state. In response to cold, the 47-bp CME and its associated bivalent chromatin feature drive the switching of active chromatin state at a recombinant gene to a Polycomb-repressed domain, conferring cold memory. We reveal a paradigm for environment-induced chromatin-state switching at bivalent loci in plants.

mTOR inhibition acts as an unexpected checkpoint in p53-mediated tumor suppression.

Here, we showed that the acetylation-defective p53-4KR mice, lacking the ability of cell cycle arrest, senescence, apoptosis, and ferroptosis, were tumor prone but failed to develop early-onset tumors. By identifying a novel p53 acetylation site at lysine K136, we found that simultaneous mutations at all five acetylation sites (p53-5KR) diminished its remaining tumor suppression function. Moreover, the embryonic lethality caused by the deficiency of mdm2 was fully rescued in the background of p535KR/5KR , but not p534KR/4KR background. p53-4KR retained the ability to suppress mTOR function but this activity was abolished in p53-5KR cells. Notably, the early-onset tumor formation observed in p535KR/5KR and p53-null mice was suppressed upon the treatment of the mTOR inhibitor. These results suggest that p53-mediated mTOR regulation plays an important role in both embryonic development and tumor suppression, independent of cell cycle arrest, senescence, apoptosis, and ferroptosis.

The Immune Landscape of Cancer.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-ß dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning.

The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

MOTIVATION: Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. In addition, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data's inherent characteristics. RESULTS: In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics. AVAILABILITY AND IMPLEMENTATION: Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC.

MARS: a motif-based autoregressive model for retrosynthesis prediction.

MOTIVATION: Retrosynthesis is a critical task in drug discovery, aimed at finding a viable pathway for synthesizing a given target molecule. Many existing approaches frame this task as a graph-generating problem. Specifically, these methods first identify the reaction center, and break a targeted molecule accordingly to generate the synthons. Reactants are generated by either adding atoms sequentially to synthon graphs or by directly adding appropriate leaving groups. However, both of these strategies have limitations. Adding atoms results in a long prediction sequence that increases the complexity of generation, while adding leaving groups only considers those in the training set, which leads to poor generalization. RESULTS: In this paper, we propose a novel end-to-end graph generation model for retrosynthesis prediction, which sequentially identifies the reaction center, generates the synthons, and adds motifs to the synthons to generate reactants. Given that chemically meaningful motifs fall between the size of atoms and leaving groups, our model achieves lower prediction complexity than adding atoms and demonstrates superior performance than adding leaving groups. We evaluate our proposed model on a benchmark dataset and show that it significantly outperforms previous state-of-the-art models. Furthermore, we conduct ablation studies to investigate the contribution of each component of our proposed model to the overall performance on benchmark datasets. Experiment results demonstrate the effectiveness of our model in predicting retrosynthesis pathways and suggest its potential as a valuable tool in drug discovery. AVAILABILITY AND IMPLEMENTATION: All code and data are available at https://github.com/szu-ljh2020/MARS.

60Co-γ Irradiation-Induced Shift and Polarity Reversal in the Triboelectric Series of Poly(ether sulfone)s.

The sensitivity of triboelectric nanogenerators (TENGs) to the surface charge density highlights the significance of triboelectric materials and their modifications. Efforts have been directed toward developing effective strategies for increasing the surface charge density, expanding the potential applications of TENGs. This study proposes the use of irradiation technology for grafting to modify the electron-donating capability of poly(ether sulfone) (PES), thereby affording a dual benefit of enhancing the surface charge density and inducing a shift in the position of PES from negative to positive within the triboelectric series. The TENG based on grafted PES has resulted in a significant 3-fold increase in surface charge density compared to that of pristine PES, reaching 263 µC m-2. The surface charge density can be further increased to 502 µC m-2 through charge pumping. Notably, irradiation technology presents advantages over chemical grafting methods, particularly in terms of sustainability and environmental friendliness. This innovative approach shows great potential in advancing the domain of TENGs.

Colloidal Synthesis of Cu3N Nanorods and Construction of Cu3N-Cu2O Heteronanostructures via Epitaxial Growth.

The synthesis of transition metal nitrides nanocrystals (TMNs NCs) has posed a significant challenge due to the limited reactivity of nitrogen sources at lower temperatures and the scarcity of available synthesis methods. In this study, we present a novel colloidal synthesis strategy for the fabrication of Cu3N nanorods (NRs). It is found that the trace oxygen (O2) plays an important role in the synthesis process. And a new mechanism for the formation of Cu3N is proposed. Subsequently, by employing secondary lateral epitaxial growth, the Cu3N-Cu2O heteronanostructures (HNs) can be prepared. The Cu3N NRs and Cu3N-Cu2O HNs were evaluated as precursor electrocatalysts for the CO2 reduction reaction (CO2RR). The Cu3N-Cu2O HNs demonstrate remarkable selectivity and stability with ethylene (C2H4) Faradaic efficiency (FE) up to 55.3%, surpassing that of Cu3N NRs. This study provides innovative insights into the reaction mechanism of colloidal synthesis of TMNs NCs and presents alternative options for designing cost-effective electrocatalysts to achieve carbon neutrality.

Oscillatory Active State of a Pd Nanocatalyst Identified by In Situ Capture of the Instantaneous Structure-Activity Change at the Atomic Scale.

Identifying the active phase with the highest activity, which is long-believed to be a steady state of the catalyst, is the basis of rational design of heterogeneous catalysis. In this work, we performed detailed in situ investigations, successfully capturing the instantaneous structure-activity change in oscillating Pd nanocatalysts during methane oxidation, which reveals an unprecedented oscillatory active state. Combining in situ quantitative environmental transmission electron microscopy and highly sensitive online mass spectrometry, we identified two distinct phases for the reaction: one where the Pd nanoparticles refill with oxygen, and the other, a period of abrupt pumping of oxygen and boosted methane oxidation within about 1 s. It is the rapid reduction process that shows the highest activity for total oxidation of methane, not a PdO or Pd steady state under the conditions applied here (methane:oxygen = 5:1). This observation challenges the traditional understanding of the active phase and requires a completely different strategy for catalyst optimization.

Lipoprotein lipids and apolipoproteins in primary immune thrombocytopenia: Results from a clinical characteristics and causal relationship verification, potential drug target identification by Mendelian randomization analyses.

Primary immune thrombocytopenia (ITP) is an acquired autoimmune disease. Cellular and systemic lipid metabolism plays a significant role in the regulation of immune cell activities. However, the role of lipoprotein lipids and apolipoproteins in ITP remains elusive. The automatic biochemistry analyser was used to measure the levels of serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-I (apoA-I), apoB, apoE and lipoprotein a [LP(a)]. Genetic variants strongly associated with circulating lipoprotein lipids and apolipoproteins (LDL-C, apoB, TG, HDL-C and apoA-I) were extracted to perform Mendelian randomization (MR) analyses. Finally, drug-target MR and passive ITP mice model was used to investigate the potential druggable targets of ITP. Levels of HDL-C, apoA-I, decreased and LP(a) increased in ITP patients compared with healthy controls. Low HDL-C was causally associated with ITP susceptibility. Through drug-target MR and animal modelling, ABCA1 was identified as a potential target to design drugs for ITP. Our study found that lipid metabolism is related to ITP. The causative association between HDL-C and the risk of ITP was also established. The study provided new evidence of the aetiology of ITP. ABCA1 might be a potential drug target for ITP.

Inhibition of TTX-S Na+ currents by a novel blocker QLS-278 for antinociception.

Genetic loss-of-function mutations of Nav1.7 channel, abundantly expressed in peripheral nociceptive neurons, cause congenital insensitivity to pain (CIP) in humans, indicating that selective inhibition of the channel may lead to potential therapy of pain disorders. In this study, we investigated a novel compound, 5-chloro-N-(cyclopropylsulfonyl)-2-fluoro-4-(2-(8-(furan-2-ylmethyl)-8-azaspiro [4.5] decan-2-yl) ethoxy) benzamide (QLS-278) that inhibits Nav1.7 channel and exhibits anti-nociceptive activity. Compound QLS-278 exhibits inactivation- and concentration-dependent inhibition of macroscopic currents of Nav1.7 channels stably expressed in HEK293 cells with an IC50 of 1.2 {plus minus} 0.2 µM. QLS-278 causes a hyperpolarization shift of the channel inactivation and delays recovery from inactivation, without an obvious effect on voltage-dependent activation. In mouse DRG neurons, QLS-278 suppresses native TTX-sensitive Nav currents and also reduces neuronal firing. Moreover, QLS-278 dose-dependently relieves neuropathic pain induced by spared nerve injury and inflammatory pain induced by formalin without significant alteration of spontaneous locomotor activity in mice. Altogether, our identification of the novel compound QLS-278 may hold developmental potential for the treatment of chronic pain. Significance Statement QLS-278, a novel voltage-gated sodium Nav1.7 channel blocker, inhibits native TTX-S Na+ current and reduces action potential firings in DRG sensory neurons. QLS-278 also exhibits antinociceptive activity in mouse models of pain, thus demonstrating potential for the development of a treatment for chronic pain.

DEMOC: a deep embedded multi-omics learning approach for clustering single-cell CITE-seq data.

Advances in single-cell RNA sequencing (scRNA-seq) technologies has provided an unprecedent opportunity for cell-type identification. As clustering is an effective strategy towards cell-type identification, various computational approaches have been proposed for clustering scRNA-seq data. Recently, with the emergence of cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), the cell surface expression of specific proteins and the RNA expression on the same cell can be captured, which provides more comprehensive information for cell analysis. However, existing single cell clustering algorithms are mainly designed for single-omic data, and have difficulties in handling multi-omics data with diverse characteristics efficiently. In this study, we propose a novel deep embedded multi-omics clustering with collaborative training (DEMOC) model to perform joint clustering on CITE-seq data. Our model can take into account the characteristics of transcriptomic and proteomic data, and make use of the consistent and complementary information provided by different data sources effectively. Experiment results on two real CITE-seq datasets demonstrate that our DEMOC model not only outperforms state-of-the-art single-omic clustering methods, but also achieves better and more stable performance than existing multi-omics clustering methods. We also apply our model on three scRNA-seq datasets to assess the performance of our model in rare cell-type identification, novel cell-subtype detection and cellular heterogeneity analysis. Experiment results illustrate the effectiveness of our model in discovering the underlying patterns of data.

scDEA: differential expression analysis in single-cell RNA-sequencing data via ensemble learning.

The identification of differentially expressed genes between different cell groups is a crucial step in analyzing single-cell RNA-sequencing (scRNA-seq) data. Even though various differential expression analysis methods for scRNA-seq data have been proposed based on different model assumptions and strategies recently, the differentially expressed genes identified by them are quite different from each other, and the performances of them depend on the underlying data structures. In this paper, we propose a new ensemble learning-based differential expression analysis method, scDEA, to produce a more stable and accurate result. scDEA integrates the P-values obtained from 12 individual differential expression analysis methods for each gene using a P-value combination method. Comprehensive experiments show that scDEA outperforms the state-of-the-art individual methods with different experimental settings and evaluation metrics. We expect that scDEA will serve a wide range of users, including biologists, bioinformaticians and data scientists, who need to detect differentially expressed genes in scRNA-seq data.

Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey.

Advances in high-throughput experimental technologies promote the accumulation of vast number of biomedical data. Biomedical link prediction and single-cell RNA-sequencing (scRNA-seq) data imputation are two essential tasks in biomedical data analyses, which can facilitate various downstream studies and gain insights into the mechanisms of complex diseases. Both tasks can be transformed into matrix completion problems. For a variety of matrix completion tasks, matrix factorization has shown promising performance. However, the sparseness and high dimensionality of biomedical networks and scRNA-seq data have raised new challenges. To resolve these issues, various matrix factorization methods have emerged recently. In this paper, we present a comprehensive review on such matrix factorization methods and their usage in biomedical link prediction and scRNA-seq data imputation. Moreover, we select representative matrix factorization methods and conduct a systematic empirical comparison on 15 real data sets to evaluate their performance under different scenarios. By summarizing the experimental results, we provide general guidelines for selecting matrix factorization methods for different biomedical matrix completion tasks and point out some future directions to further improve the performance for biomedical link prediction and scRNA-seq data imputation.

Chirality-Driven Orbital Angular Momentum and Circular Dichroism in CoSi.

Chiral crystals and molecules were recently predicted to form an intriguing platform for unconventional orbital physics. Here, we report the observation of chirality-driven orbital textures in the bulk electronic structure of CoSi, a prototype member of the cubic B20 family of chiral crystals. Using circular dichroism in soft x-ray angle-resolved photoemission, we demonstrate the formation of a bulk orbital-angular-momentum texture and monopolelike orbital-momentum locking that depends on crystal handedness. We introduce the intrinsic chiral circular dichroism, icCD, as a differential photoemission observable and a natural probe of chiral electron states. Our findings render chiral crystals promising for spin-orbitronics applications.

CD276 enhances sunitinib resistance in clear cell renal cell carcinoma by promoting DNA damage repair and activation of FAK-MAPK signaling pathway.

OBJECTIVE: This study aimed to explore the effect of CD276 expression on the sunitinib sensitivity of clear cell renal cell carcinoma (ccRCC) cell and animal models and the potential mechanisms involved. METHODS: CD276 expression levels of ccRCC and normal samples were analyzed via online databases and real-time quantitative PCR (RT-qPCR). CD276 was knocked down in ccRCC cell models (sunitinib-resistant 786-O/R cells and sunitinib-sensitive 786-O cells) using shRNA transfection, and the cells were exposed to a sunitinib (2 µM) environment. Cells proliferation was then analyzed using MTT assay and colony formation experiment. Alkaline comet assay, immunofluorescent staining, and western blot experiments were conducted to assess the DNA damage repair ability of the cells. Western blot was also used to observe the activation of FAK-MAPK pathway within the cells. Finally, a nude mouse xenograft model was established and the nude mice were orally administered sunitinib (40 mg/kg/d) to evaluate the in vivo effects of CD276 knockdown on the therapeutic efficacy of sunitinib against ccRCC. RESULTS: CD276 was significantly upregulated in both ccRCC clinical tissue samples and cell models. In vitro experiments showed that knocking down CD276 reduced the survival rate, IC50 value, and colony-forming ability of ccRCC cells. Knocking down CD276 increased the comet tail moment (TM) values and γH2AX foci number, and reduced BRCA1 and RAD51 protein levels. Knocking down CD276 also decreased the levels of p-FAK, p-MEK, and p-ERK proteins. CONCLUSION: Knocking down CD276 effectively improved the sensitivity of ccRCC cell and animal models to sunitinib treatment.

Mendelian randomization of circulating proteome identifies IFN-γ as a druggable target in aplastic anemia.

BACKGROUND: Aplastic anemia (AA) is a kind of bone marrow failure (BMF) characterized by pancytopenia with hypoplasia/aplasia of bone marrow. Immunosuppressive therapy and bone marrow transplantation are effective methods to treat severe aplastic anemia. However, the efficacy is limited by complications and the availability of suitable donors. This study aimed to determine whether any circulating druggable protein levels may have causal effects on AA and provide potential novel drug targets for AA. METHODS: Genetic variants strongly associated with circulating druggable protein levels to perform Mendelian randomization (MR) analyses were used. The effect of these druggable protein levels on AA risk was measured using the summary statistics from a large-scale proteomic genome-wide association study (GWAS) and FinnGen database ( https://www.finngen.fi/en/access_results ). Multivariable MR analyses were performed to statistically adjust for potential confounders, including platelet counts, reticulocyte counts, neutrophil counts, and proportions of hematopoietic stem cells. RESULTS: The data showed that higher level of circulating IFN-γ levels was causally associated with AA susceptibility. The causal effects of circulating IFN-γ levels on the AA were broadly consistent, when adjusted for platelet counts, reticulocyte counts, neutrophil counts and proportions of hematopoietic stem cells. CONCLUSIONS: High levels of circulating IFN-γ levels might increase the risk of AA and might provide a potential novel target for AA.

Mechanisms and clinical landscape of N6-methyladenosine (m6A) RNA modification in gastrointestinal tract cancers.

Epigenetics encompasses reversible and heritable chemical modifications of non-nuclear DNA sequences, including DNA and RNA methylation, histone modifications, non-coding RNA modifications, and chromatin rearrangements. In addition to well-studied DNA and histone methylation, RNA methylation has emerged as a hot topic in biological sciences over the past decade. N6-methyladenosine (m6A) is the most common and abundant modification in eukaryotic mRNA, affecting all RNA stages, including transcription, translation, and degradation. Advances in high-throughput sequencing technologies made it feasible to identify the chemical basis and biological functions of m6A RNA. Dysregulation of m6A levels and associated modifying proteins can both inhibit and promote cancer, highlighting the importance of the tumor microenvironment in diverse biological processes. Gastrointestinal tract cancers, including gastric, colorectal, and pancreatic cancers, are among the most common and deadly malignancies in humans. Growing evidence suggests a close association between m6A levels and the progression of gastrointestinal tumors. Global m6A modification levels are substantially modified in gastrointestinal tumor tissues and cell lines compared to healthy tissues and cells, possibly influencing various biological behaviors such as tumor cell proliferation, invasion, metastasis, and drug resistance. Exploring the diagnostic and therapeutic potential of m6A-related proteins is critical from a clinical standpoint. Developing more specific and effective m6A modulators offers new options for treating these tumors and deeper insights into gastrointestinal tract cancers.

The future of blood services amid a tight balance between the supply and demand of blood products: Perspectives from the ISBT Young Professional Council.

BACKGROUND AND OBJECTIVES: Blood services manage the increasingly tight balance between the supply and demand of blood products, and their role in health research is expanding. This review explores the themes that may define the future of blood banking. MATERIALS AND METHODS: We reviewed the PubMed database for articles on emerging/new blood-derived products and the utilization of blood donors in health research. RESULTS: In high-income countries (HICs), blood services may consider offering these products: whole blood, cold-stored platelets, synthetic blood components, convalescent plasma, lyophilized plasma and cryopreserved/lyophilized platelets. Many low- and middle-income countries (LMICs) aim to establish a pool of volunteer, non-remunerated blood donors and wean themselves off family replacement donors; and many HICs are relaxing the deferral criteria targeting racial and sexual minorities. Blood services in HICs could achieve plasma self-sufficiency by building plasma-dedicated centres, in collaboration with the private sector. Lastly, blood services should expand their involvement in health research by establishing donor cohorts, conducting serosurveys, studying non-infectious diseases and participating in clinical trials. CONCLUSION: This article provides a vision of the future for blood services. The introduction of some of these changes will be slower in LMICs, where addressing key operational challenges will likely be prioritized.

Air Quality, Health, and Equity Benefits of Carbon Neutrality and Clean Air Pathways in China.

In the pursuit of carbon neutrality, China's 2060 targets have been largely anchored in reducing greenhouse gas emissions, with less emphasis on the consequential benefits for air quality and public health. This study pivots to this critical nexus, exploring how China's carbon neutrality aligns with the World Health Organization's air quality guidelines (WHO AQG) regarding fine particulate matter (PM2.5) exposure. Coupling a technology-rich integrated assessment model, an emission-concentration response surface model, and exposure and health assessment, we find that decarbonization reduces sulfur dioxide (SO2), nitrogen oxides (NOx), and PM2.5 emissions by more than 90%; reduces nonmethane volatile organic compounds (NMVOCs) by more than 50%; and simultaneously reduces the disparities across regions. Critically, our analysis reveals that further targeted reductions in air pollutants, notably NH3 and non-energy-related NMVOCs, could bring most Chinese cities into attainment of WHO AQG for PM2.5 5 to 10 years earlier than the pathway focused solely on carbon neutrality. Thus, the integration of air pollution control measures into carbon neutrality strategies will present a significant opportunity for China to attain health and environmental equality.